| 引用本文: | 张勇刚,王刚,黄玉龙,李宁.递推更新高斯粒子滤波器[J].控制理论与应用,2016,33(3):353~360.[点击复制] |
| ZHANG Yong-gang,WANG Gang,HUANG Yu-long,LI Ning.Recursive update Gaussian particle filter[J].Control Theory and Technology,2016,33(3):353~360.[点击复制] |
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| 递推更新高斯粒子滤波器 |
| Recursive update Gaussian particle filter |
| 摘要点击 3642 全文点击 2596 投稿时间：2015-05-14 修订日期：2015-10-12 |
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| DOI编号 10.7641/CTA.2016.50402 |
| 2016,33(3):353-360 |
| 中文关键词 高斯滤波器 粒子滤波 递推算法 非线性滤波 |
| 英文关键词 Gaussian filter particle filter recursive algorithm nonlinear filtering |
| 基金项目 国家自然科学基金项目(61201409, 61371173), 中国博士后科学基金(2013M530147, 2014T70309), 黑龙江省博士后基金(LBH–Z13052, LBH– TZ0505), 哈尔滨工程大学中央高校基本科研业务费专项基金(HEUCFQ20150407)资助. |
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| 中文摘要 |
| 传统高斯粒子滤波算法(Gaussian particle Filter, GPF)中, 粒子的重要性密度函数是由高斯滤波器结合当前
最新量测来构建的. 由于传统高斯滤波器在量测更新阶段直接利用量测对状态进行线性更新, 在某些条件下会导
致所构建的重要性密度函数并不能很好地近似状态真实分布. 为了解决这一问题, 结合递推更新的思想, 本文推导
出了递推更新高斯滤波器(recursive update Gaussian filter, RUGF)的一般结构. 并在此基础上, 选用RUGF来构建粒子
滤波的重要性密度函数, 从而提出了基于递推更新的高斯粒子滤波算法(recursive update gaussian particle filter,
RUGPF). 仿真表明, 在非线性系统状态估计问题中,递推更新可以很好的利用量测信息, 相比于传统的GPF, 本文所
提出的RUGPF滤波算法可以提供更高精度的估计结果. |
| 英文摘要 |
| In traditional Gaussian particle filter (GPF), sample importance density function is constructed through combining
the latest measurements based on Gaussian filter (GF). However, in measurement update of the traditional GF, since
the measurement value is assimilated directly based on the linear update rule, the constructed sample importance density
function may not be approximate to the real posterior distribution under certain conditions. To solve this problem, we
propose a new recursive update GF (RUGF) based on the recursive update idea and give out its general framework. On this
basis, a new sample importance density function is constructed by using RUGF, based on which a new recursive update
GPF (RUGPF) can be derived. Simulation results demonstrate that recursive update idea can assimilate the measurement
information commendably, and compared with traditional GPF, the proposed filter has higher estimation accuracy for state
estimation in nonlinear systems. |
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